EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 341 (2025) EPJ Web Conf., 341 (2025) 01015 References
Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01015
Number of page(s) 13
DOI https://doi.org/10.1051/epjconf/202534101015
Published online 20 November 2025
  1. P. Lata and S. Singh, "Intrusion detection system in cloud environment: Literature review," Materials Today: Proceedings, vol. 51, pp. 1520-1526, 2022. doi: 10.1016/j.matpr.2021.07.336 [Google Scholar]
  2. M. Nasim, A. Javed, A. Ahmad, and N. Javaid, "A systematic literature review on intrusion detection and prevention systems in cloud computing," Information Retrieval Journal, vol. 28, no. 2, pp. 233-257, 2025. doi: 10.1007/s10791-025-09641-y [Google Scholar]
  3. J. Gong, X. Zhou, Y. Chen, and H. Jin, "Acceleration schemes for fully homomorphic encryption: A survey," Cybersecurity, vol. 6, no. 5, pp. 1-23, 2023. doi: 10.1186/s42400-023-00187-4 [Google Scholar]
  4. A. Gahi, M. Guennoun, and K. El-Khatib, "Homomorphic encryption for database privacy in cloud computing," Procedia Computer Science, vol. 94, pp. 371-376, 2021. doi: 10.1016/j.procs.2021.01.112 [Google Scholar]
  5. Y. Zhang, R. Deng, and D. W. Chadwick, "Machine learning approaches for intrusion detection in cloud computing: A survey," IEEE Access, vol. 9, pp. 157-176, 2021. doi: 10.1109/ACCESS.2021.3058974 [Google Scholar]
  6. K. J. Singh, A. Sharma, and R. Kumar, "Federated learning and anomaly detection for cloud database security: A review," Future Generation Computer Systems, vol. 142, pp. 414-430, 2023. doi: 10.1016/j.future.2023.03.010 [Google Scholar]
  7. M. A. AlZain, B. Soh, and E. Pardede, "Cloud computing security: From single to multi-clouds," in Proc. IEEE 45th Hawaii Int. Conf. System Sciences (HICSS), 2012, pp. 5490-5499. doi: 10.1109/HICSS.2012.619 [Google Scholar]
  8. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility," Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009. doi: 10.1016/j.future.2008.12.001 [CrossRef] [Google Scholar]
  9. A. Singh and K. Chatterjee, "Cloud security issues and challenges: A survey," Journal of Network and Computer Applications, vol. 79, pp. 88-115, 2017. doi: 10.1016/j.jnca.2016.11.027 [Google Scholar]
  10. Z. Brakerski, C. Gentry, and V. Vaikuntanathan, "(Leveled) fully homomorphic encryption without bootstrapping," ACM Transactions on Computation Theory (TOCT), vol. 6, no. 3, pp. 1-36, 2014. doi: 10.1145/2633600 [Google Scholar]
  11. X. Xu, I. Weber, and M. Staples, Architecture for Blockchain Applications. Cham, Switzerland: Springer, 2019. doi: 10.1007/978-3-030-03035-3 [Google Scholar]
  12. M. A. Ferrag, L. Maglaras, H. Janicke, J. Jiang, and L. Shu, "A systematic review of data protection and privacy preservation schemes for smart grid communications," Sustainable Cities and Society, vol. 38, pp. 806-835, 2018. doi: 10.1016/j.scs.2018.01.001 [Google Scholar]
  13. A. N. Khan, M. L. Mat Kiah, S. U. Khan, and S. A. Madani, "Towards secure mobile cloud computing: A survey," Future Generation Computer Systems, vol. 29, no. 5, pp. 1278-1299, 2013. doi: 10.1016/j.future.2012.08.003 [Google Scholar]
  14. W. Stallings, Cryptography and Network Security: Principles and Practice, 8th ed. Boston, MA, USA: Pearson, 2022. [Google Scholar]
  15. A. K. Das, S. Zeadally, and D. He, "Taxonomy and analysis of security protocols for Internet of Things," Future Generation Computer Systems, vol. 89, pp. 110-125, 2018. doi: 10.1016/j.future.2018.06.016 [Google Scholar]
  16. C. Gentry, "Fully homomorphic encryption using ideal lattices," in Proc. 41st ACM Symp. Theory of Computing (STOC), 2009, pp. 169-178. doi: 10.1145/1536414.1536440 [Google Scholar]
  17. J. Gong, X. Zhou, Y. Chen, and H. Jin, "Acceleration schemes for fully homomorphic encryption: A survey," Cybersecurity, vol. 6, no. 5, pp. 1-23, 2023. doi: 10.1186/s42400-023-00187-4 [Google Scholar]
  18. V. Goyal, O. Pandey, A. Sahai, and B. Waters, "Attribute-based encryption for finegrained access control of encrypted data," in Proc. 13th ACM Conf. Computer and Communications Security (CCS), 2006, pp. 89-98. doi: 10.1145/1180405.1180418 [Google Scholar]
  19. R. Curtmola, J. Garay, S. Kamara, and R. Ostrovsky, "Searchable symmetric encryption: Improved definitions and efficient constructions," Journal of Computer Security, vol. 19, no. 5, pp. 895-934, 2011. doi: 10.3233/JCS-2009-0391 [Google Scholar]
  20. X. Xu, I. Weber, and M. Staples, Architecture for Blockchain Applications. Cham, Switzerland: Springer, 2019. doi: 10.1007/978-3-030-03035-3 [Google Scholar]
  21. S. Subashini and V. Kavitha, "A survey on security issues in service delivery models of cloud computing," Journal of Network and Computer Applications, vol. 34, no. 1, pp. 1-11, 2011. doi: 10.1016/j.jnca.2010.07.006 [Google Scholar]
  22. M. Almseidin, M. Al-Kasassbeh, S. Alauthman, and M. Al-Qerem, "An evaluation of machine learning methods for intrusion detection systems," Security and Communication Networks, vol. 2017, pp. 1-12, 2017. doi: 10.1155/2017/7089801 [Google Scholar]
  23. P. Lata and S. Singh, "Intrusion detection system in cloud environment: Literature review," Materials Today: Proceedings, vol. 51, pp. 1520-1526, 2022. doi: 10.1016/j.matpr.2021.07.336 [Google Scholar]
  24. D. E. Denning, "An intrusion-detection model," IEEE Transactions on Software Engineering, vol. SE-13, no. 2, pp. 222-232, 1987. doi: 10.1109/TSE.1987.232894 [Google Scholar]
  25. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in Proc. IEEE Symp. Computational Intelligence for Security and Defense Applications (CISDA), 2009, pp. 1-6. doi: 10.1109/CISDA.2009.5356528 [Google Scholar]
  26. I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterization," in Proc. 4th Int. Conf. Information Systems Security and Privacy (ICISSP), 2018, pp. 108-116. doi: 10.5220/0006639801080116 [Google Scholar]
  27. H. Hindy, E. Bayne, D. Brosset, A. Seeam, C. Tachtatzis, R. Atkinson, and X. Bellekens, "Machine learning and cybersecurity: A survey of supervised learning methods," Computers & Security, vol. 97, p. 101861, 2020. doi: 10.1016/j.cose.2020.101861 [Google Scholar]
  28. S. Shone, D. N. Ngoc, V. D. Phai, and Q. Shi, "A deep learning approach to network intrusion detection," IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41-50, 2018. doi: 10.1109/TETCI.2017.2772792 [CrossRef] [Google Scholar]
  29. M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, A. Atiewi, and A. Razaque, "Deep recurrent neural network for IoT intrusion detection system," Simulation Modelling Practice and Theory, vol. 101, p. 102031, 2020. doi: 10.1016/j.simpat.2019.102031 [Google Scholar]
  30. V. C. Hu, D. Ferraiolo, and D. Kuhn, Assessment of Access Control Systems. Gaithersburg, MD, USA: NIST, 2006. doi: 10.6028/NIST.SP.800-36 [Google Scholar]
  31. S. Shitharth, "An enhanced security framework for detecting and preventing insider threats in cloud environment," Computers & Security, vol. 77, pp. 493-506, 2018. doi: 10.1016/j.cose.2018.05.010 [Google Scholar]
  32. C. Dwork and A. Roth, "The algorithmic foundations of differential privacy," Foundations and Trends in Theoretical Computer Science, vol. 9, nos. 3-4, pp. 211407, 2014. doi: 10.1561/0400000042 [Google Scholar]
  33. Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: Concept and applications," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1-19, 2019. doi: 10.1145/3298981 [Google Scholar]
  34. Z. Q. He, Z. Yan, and R. H. Deng, "Reinforcement learning based defense strategies against advanced persistent threats in cyber-physical systems," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1840-1855, 2021. doi: 10.1109/TIFS.2020.3044233 [Google Scholar]
  35. H. Hindy, E. Bayne, D. Brosset, A. Seeam, C. Tachtatzis, R. Atkinson, and X. Bellekens, "A taxonomy and survey of intrusion detection system design techniques, network threats and datasets," Computers & Security, vol. 102, p. 102166, 2021. doi: 10.1016/j.cose.2020.102166 [Google Scholar]
  36. J. Bethencourt, A. Sahai, and B. Waters, "Ciphertext-policy attribute-based encryption," in Proc. IEEE Symp. Security and Privacy (S & P), 2007, pp. 321-334. doi: 10.1109/SP.2007.11 [Google Scholar]
  37. D. Cash, P. Grubbs, J. Perry, and T. Ristenpart, "Leakage-abuse attacks against searchable encryption," in Proc. ACM Conf. Computer and Communications Security (CCS), 2015, pp. 668-679. doi: 10.1145/2810103.2813700 [Google Scholar]
  38. M. Abadi et al., "Deep learning with differential privacy," in Proc. ACM CCS, 2016, pp. 308-318. doi: 10.1145/2976749.2978318 [Google Scholar]
  39. K. Bonawitz et al., "Practical secure aggregation for privacy-preserving machine learning," in Proc. ACM CCS, 2017, pp. 1175-1191. doi: 10.1145/3133956.3133982 [Google Scholar]
  40. N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for research with network traffic and normal/attack activities," in Proc. Military Communications and Information Systems Conf. (MilCIS), 2015, pp. 1-6. doi: 10.1109/MilCIS.2015.7348942 [Google Scholar]
  41. I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterization," in Proc. 4th Int. Conf. Information Systems Security and Privacy (ICISSP), 2018, pp. 108-116. doi: 10.5220/0006639801080116 [Google Scholar]
  42. T. Croman et al., "On scaling decentralized blockchains," in Proc. Financial Cryptography and Data Security, 2016, pp. 106-125. doi: 10.1007/978-3-662-53357-4_8 [Google Scholar]
  43. R. A. Popa, C. Redfield, N. Zeldovich, and H. Balakrishnan, "CryptDB: Protecting confidentiality with encrypted query processing," in Proc. ACM Symp. Operating Systems Principles (SOSP), 2011, pp. 85-100. doi: 10.1145/2043556.2043566 [Google Scholar]
  44. J. Gong, X. Zhou, Y. Chen, and H. Jin, "Acceleration schemes for fully homomorphic encryption: A survey," Cybersecurity, vol. 6, no. 5, pp. 1-23, 2023. doi: 10.1186/s42400-023-00187-4 [Google Scholar]
  45. Z. Brakerski, C. Gentry, and V. Vaikuntanathan, "(Leveled) fully homomorphic encryption without bootstrapping," ACM Transactions on Computation Theory (TOCT), vol. 6, no. 3, pp. 1-36, 2014. doi: 10.1145/2633600 [Google Scholar]
  46. H. Hindy, E. Bayne, D. Brosset, A. Seeam, C. Tachtatzis, R. Atkinson, and X. Bellekens, "Machine learning and cybersecurity: A survey of supervised learning methods," Computers & Security, vol. 97, p. 101861, 2020. doi: 10.1016/j.cose.2020.101861 [Google Scholar]
  47. K. Bonawitz et al., "Practical secure aggregation for privacy-preserving machine learning," in Proc. ACM CCS, 2017, pp. 1175-1191. doi: 10.1145/3133956.3133982 [Google Scholar]
  48. T. Croman et al., "On scaling decentralized blockchains," in Proc. Financial Cryptography and Data Security, 2016, pp. 106-125. doi: 10.1007/978-3-662-53357-4_8 [Google Scholar]
  49. W. Samek, T. Wiegand, and K. Müller, "Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models," IT Professional, vol. 21, no. 4, pp. 31-41, 2019. doi: 10.1109/MITP.2019.2912186 [Google Scholar]
  50. F. Ahmad, A. M. A. Haider, and A. Alsmadi, "Explainable artificial intelligence for cybersecurity: State-of-the-art, challenges and future directions," Journal of Information Security and Applications, vol. 73, p. 103514, 2023. doi: 10.1016/j.jisa.2023.103514 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.